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NEW QUESTION 43
A company developed a new elections reporting website that uses Amazon Kinesis Data Firehose to deliver full logs from AWS WAF to an Amazon S3 bucket. The company is now seeking a low-cost option to perform this infrequent data analysis with visualizations of logs in a way that requires minimal development effort.
Which solution meets these requirements?

• A. Create an AWS Lambda function to convert the logs into .csv format. Then add the function to the Kinesis Data Firehose transformation configuration. Use Amazon Redshift to perform ad-hoc analyses of the logs using SQL queries and use Amazon QuickSight to develop data visualizations.
• B. Use an AWS Glue crawler to create and update a table in the Glue data catalog from the logs. Use Athena to perform ad-hoc analyses and use Amazon QuickSight to develop data visualizations.
• C. Create a second Kinesis Data Firehose delivery stream to deliver the log files to Amazon Elasticsearch Service (Amazon ES). Use Amazon ES to perform text-based searches of the logs for ad-hoc analyses and use Kibana for data visualizations.
• D. Create an Amazon EMR cluster and use Amazon S3 as the data source. Create an Apache Spark job to perform ad-hoc analyses and use Amazon QuickSight to develop data visualizations.

Answer: B

Explanation:
Explanation
https://aws.amazon.com/blogs/big-data/analyzing-aws-waf-logs-with-amazon-es-amazon-athena-and-amazon-qu

NEW QUESTION 44
A company has a data warehouse in Amazon Redshift that is approximately 500 TB in size. New data is imported every few hours and read-only queries are run throughout the day and evening. There is a particularly heavy load with no writes for several hours each morning on business days. During those hours, some queries are queued and take a long time to execute. The company needs to optimize query execution and avoid any downtime.
What is the MOST cost-effective solution?

• A. Use a snapshot, restore, and resize operation. Switch to the new target cluster.
• B. Enable concurrency scaling in the workload management (WLM) queue.
• C. Add more nodes using the AWS Management Console during peak hours. Set the distribution style to ALL.
• D. Use elastic resize to quickly add nodes during peak times. Remove the nodes when they are not needed.

Answer: B

Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/cm-c-implementing-workload-management.html

NEW QUESTION 45
A company has an application that uses the Amazon Kinesis Client Library (KCL) to read records from a Kinesis data stream.
After a successful marketing campaign, the application experienced a significant increase in usage. As a result, a data analyst had to split some shards in the data stream. When the shards were split, the application started throwing an ExpiredIteratorExceptions error sporadically.
What should the data analyst do to resolve this?

• A. Decrease the provisioned write capacity units assigned to the stream's Amazon DynamoDB table.
• B. Increase the provisioned write capacity units assigned to the stream's Amazon DynamoDB table.
• C. Increase the number of threads that process the stream records.
• D. Increase the provisioned read capacity units assigned to the stream's Amazon DynamoDB table.

Answer: B

NEW QUESTION 46
A financial company uses Amazon S3 as its data lake and has set up a data warehouse using a multi-node Amazon Redshift cluster. The data files in the data lake are organized in folders based on the data source of each data file. All the data files are loaded to one table in the Amazon Redshift cluster using a separate COPY command for each data file location. With this approach, loading all the data files into Amazon Redshift takes a long time to complete. Users want a faster solution with little or no increase in cost while maintaining the segregation of the data files in the S3 data lake.
Which solution meets these requirements?

• A. Use Amazon EMR to copy all the data files into one folder and issue a COPY command to load the data into Amazon Redshift.
• B. Use an AWS Glue job to copy all the data files into one folder and issue a COPY command to load the data into Amazon Redshift.
• C. Create a manifest file that contains the data file locations and issue a COPY command to load the data into Amazon Redshift.
• D. Load all the data files in parallel to Amazon Aurora, and run an AWS Glue job to load the data into Amazon Redshift.

Answer: C

Explanation:
Explanation
https://docs.aws.amazon.com/redshift/latest/dg/loading-data-files-using-manifest.html "You can use a manifest to ensure that the COPY command loads all of the required files, and only the required files, for a data load"

NEW QUESTION 47
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